The present invention relates to mobile phone applications and methods of use thereof. In particular, the invention relates to a software system for monitoring and reporting upon the performance of mobile phone applications. The most novel element of this invention is the crowd-source dynamic feedback mechanism which pushes all of the app quality and performance data to the cloud based servers for analysis and then pushes aggregated crowd-source statistics, flagged app alerts and recommendations down to the consumer based on their specific applications, operating system version, device and wireless carrier network they are on.
A significant fraction of mobile phone applications (hereinafter referred to as “apps”) are not efficient, optimized, or safe for mobile device performance and for the quality of a consumer's user experience. A leading cause of this problem is that a vast majority of apps in the iPhone and Android markets are designed and coded by hobbyists or other programming amateurs who do not employ sufficient quality controls. To further the problem in the Android market there are over 10,000 different device types on over 300 carrier networks, each of which performs differently. The largest Android app market, the Google Play Store, is growing at an enormous rate of approximately 1000 new apps added into the market every day. The market for iPhone apps is experiencing similar growth. The vast majority of app developers do not have the capability to properly test their apps to work on all of the different devices and carrier networks.
Flagged apps are those that are poorly designed, poorly optimized, poorly quality tested, and/or unsafe, cause consumer frustration and result in device returns for Wireless Carriers and Device OEMs. The problem of flagged apps concerns performance and quality. Additional examples of flagged apps would be malware or viruses infecting apps. Sophisticated mobile phone users deal with flagged apps by downloading 10 or more app performance and quality monitoring tools. These existing app monitoring tools enable sophisticated users to track their applications across essential performance and quality metrics. M2AppMonitor is designed to provide a complete solution, eliminating the need for users to download and install multiple applications. More importantly, the M2AppMonitor data that is gathered, is pushed to the cloud on a timed basis, integrated with data from other M2AppMonitor users and then crowd-sourced statistics, app recommendations and flagged app alerts are pushed to the consumers based on their applications, operating system version, device type and carrier network.
There has been a tremendous push in technology and big data statistical analysis to infer a user's profile and demographics from the user's smartphone behavior. This analysis can identify specific company products and target marketing in a stealthy manner. For example, U.S. Pat. No. 8,631,122 issued to Kadam, et. al. (“Kadam”) discloses a method of determining demographics based on user interactions. Kadam relies on reviewing social media and scraping together data from “likes” and other relational behavior to analyze and make predictive product marketing information. While it is possible to make relatively accurate assumptions about a user's demographics it does not require any guess work if the software application just makes the demographics data entry available for a user to enter voluntarily. And, the computing power, overall energy consumption is reduced, and there is a lower likelihood of errors because there is no guessing at the user's profile. Also, in U.S. patent publication number US2010/0203876 filed by Krishnaswamy discloses a method of inferring user profile properties based upon mobile device usage. Again, similar principles are used to guess at a user's profile. Thus, there is a need to reduce power consumption and increase predictive matching based on demographics.
The present invention reduces the power consumption and inaccuracies of guessing at a smart phone user's profile and demographics. In a preferred embodiment a user may provide their profile and demographics directly into the M2AppMonitor. Based on the user profile and demographics the M2AppMonitor cloud based servers will recommend software apps based upon the user's profile. For example, age and sex are important for targeting specific markets such as video games for young males or contraception for young women. Based on crowd source information from other users it will be possible to identify users that are likely to adopt or purchase suggested software apps for the user's specific smart device.
The above referenced patents and patent applications are incorporated herein by reference in their entirety. Furthermore, where a definition or use of a term in a reference, which is incorporated by reference herein, is inconsistent or contrary to the definition of that term provided herein, the definition of that term provided herein applies and the definition of that term in the reference does not apply.